The long-term objective of this research is the development of a general computational model of perceptual categorization and memory, which interrelates performance across a variety of tasks, including classification, identification, and old-new recognition. The present project is organized around the continued development and testing of Nosofsky and Palmeri's (1997) exemplar-based random walk (EBRW) model. According to the EBRW, people represent categories by storing individual exemplars in memory. The exemplars are retrieved from memory based on how similar they are to presented test objects. These retrieved exemplars drive a random-walk process for making classification decisions. The EBRW goes beyond previous work by providing a detailed processing account of the time course of classification decision making. The first specific aim of the newly proposed research is to develop sharp contrasts between the response-time predictions from the EBRW and those of some competing models of classification, including prototype and decision-boundary models. A second aim is to extend the EBRW to account for the time course of old-new recognition decision making. A third aim is to develop and test an extended version of the EBRW that will enable the model to account for distinctiveness effects in old-new recognition. The general approach involves modeling of data from a variety of experimental paradigms that collect both response-time and choice-probability data in tasks of classification and recognition. Understanding the fundamental processes of perceptual categorization and recognition is one of the central goals of research in memory and cognition. A direct health-related application of the present work would be to provide information about how radiologists make disease classifications on the basis of imperfect information contained in X-ray displays, with the ultimate goal of developing training techniques and computer technology to assist in radiological decision making.